data visualization is a halfway house


(Image credit: A.Koblin for RadioHead)

This is a phrase that has stuck with me since Tim O’Reilly uttered some form of it two years ago.  Tim was talking about online cartography, saying it’s not the maps that matter: it’s getting to our destination.  Maps are a half-step short of that goal.  And in a world of navigational algorithms and self-driving cars, maps become less useful as tools.

Likewise, data visualization is a halfway house: a stopping place on the path from data to decision.

The explosion in interest in data visualization over the last couple of years — witness the popularity of blogs like; companies like Tableau,, and; and the maturation of Feltron-like infographics as mass media — is a powerful and important trend.  We are long overdue to make the leap into a post-spreadsheet era, and human brains are far better equipped to process pictures than mind-numbing columns of figures.

But data visualizations still require human analysts to react and kick off another action, if they are to be useful.

Worse, too much data visualization can prompt decision fatigue.  An interactive visualization of my weight, BMI, and body fat index is nice — but I’ve never logged into my scale’s online dashboard.  A ”hey, stop eating so much” text alert, or a vibrating wrist-band to get me moving, is better.  The best user interfaces don’t make us think, they help us act.

As the planet become more fully instrumented and online, from cars to cash registers to coffee pots, we find ourselves swimming in a rising tide of digital data.  We can and should seek refuge in the harbor of data visualization, where analysts surface and explore insights with with choropleths, tree maps, Sankey diagrams, and other species of story-telling shapes.

But the real revolution is at work in the digital underground, with decision chains of algorithms exchanging data, silently singing each to each, surfacing only occasionally with actions: an equity trade, a digital ad, a left turn, a dimmed street light.  These digital undergrounds, teaming with artificial life, are found on Wall Street, in online mediawithin warehouses, and across electrical grids.

These algorithms don’t require data visualizations, they consume those mind-numbing columns of figures in milliseconds.  This is the realm of mathematics and statistics, machine learning and signal processing, and the hackers of these algorithms are the econometricians, neuroscientists, and applied physicists called data scientists.  If visualization is the light side of data science, machine learning is its dark side:  black box models whose mechanisms aren’t easily visualized or interpretable, except that they work.  Renaissance Technologies hasn’t conquered Wall St. with pretty pictures, they’ve done it with better trades.

Likewise, the winners in the Big Data era will focus less on bar charts and more on  actions: helping businesses set prices, cities move citizens, and people be healthier.

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